The use of a multivariate statistical procedure in analysing the germination process of two bean cultivars, compared with a univariate approach

Abstract Several studies on plant physiology are aimed at describing or assessing seed germination processes under laboratory conditions. With respect to seed germination of crop species, some statistical complexities have been discussed, but they have not been developed much in practice. That is, such discussions are not as common as in other areas of plant biology. Additionally, the current literature that is concerned directly with the application of statistics in seed germination indicates that simple and well-known statistical procedures still merit further consideration. Regarding the use of multivariate statistical methods in agriculture, several field studies have used such procedures as a means of clarifying some underlying ecological principles that govern crop production. Nonetheless, multivariate tests have not been widely employed in germination experiments. Therefore, in the present study a simple multivariate statistical procedure (Hotelling's T 2 statistic) was utilised in order to compare two common bean cultivars, using germination indices as variables. The outcome derived from the multivariate approach was compared with that obtained from the utilisation of the univariate t test. The simultaneous application of both methods (that is, the classical univariate t test and the multivariate T 2 test) showed that the outcomes may well depend on the approach utilised

Artificial neural networks: A novel approach to analysing the nutritional ecology of a blowfly species, Chrysomya megacephala

Bionomic features of blowflies may be clarified and detailed by the deployment of appropriate modelling techniques such as artificial neural networks, which are mathematical tools widely applied to the resolution of complex biological problems. The principal aim of this work was to use three well-known neural networks, namely Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Adaptive Neural Network-Based Fuzzy Inference System (ANFIS), to ascertain whether these tools would be able to outperform a classical statistical method (multiple linear regression) in the prediction of the number of resultant adults (survivors) of experimental populations of Chrysomya megacephala (F.) (Diptera: Calliphoridae), based on initial larval density (number of larvae), amount of available food, and duration of immature stages. The coefficient of determination (R(2)) derived from the RBF was the lowest in the testing subset in relation to the other neural networks, even though its R2 in the training subset exhibited virtually a maximum value. The ANFIS model permitted the achievement of the best testing performance. Hence this model was deemed to be more effective in relation to MLP and RBF for predicting the number of survivors. All three networks outperformed the multiple linear regression, indicating that neural models could be taken as feasible techniques for predicting bionomic variables concerning the nutritional dynamics of blowflies.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Methodological difficulties of conducting agroecological studies from a statistical perspective

André Bianconi, Tommy Dalgaard, Bryan F. J. Manly, José S. Govone, Michael J. Watts, Peter Nkala, Gustavo Habermann, Yanbo Huang, and Adriane B. S. Serapiã

Bioconcentration of Cd and Pb by the River Crab Trichodactylus fluviatilis (Crustacea: Decapoda)

The bioconcentration of cadmium and lead by the freshwater crab Trichodactylus fluviatilis was evaluated. Thirty animals were exposed to 200 mu g L(-1) of cadmium and lead for 7, 14 and 21 days. Both metals were determined in gills, hepatopancreas and muscle after dissection and digestion by inductively coupled plasma optical emission spectrometry. Lead was detected only in gills, but without significant difference among different exposure periods. Cadmium was found in all tissues after exposure. Significant differences among cadmium concentrations in animals exposed for different periods suggest an accumulation process, with concentration stabilized after 14 days. Fractionation of free (or labile) cadmium and cadmium protein (possibly metallothionein) (Cd-P) in gills and hepatopancreas were carried out to assess the cadmium transference and storage in the tissues using a solid phase extraction procedure with Saccharomyces cerevisae. Fractionation of free (or labile) cadmium and cadmium protein (possibly metallothionein) (Cd-P) in animals exposed to 200 mu g L(-1) for 21 days in gills and hepatopancreas were carried to assess the cadmium transference and storage in the tissues using a solid phase extraction procedure with Saccharomyces cerevisae. In gills, cadmium was found mainly in the free form, while in hepatopancreas the metal was found mainly bound to the protein (Cd-P). It may be inferred that, absorbed through gills, cadmium was transferred and stored in the hepatopancreas.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq